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<h4>Scaling Components </h4>

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<p>For each data-set, the component images are compared with a reference image 
like aggregate ICA image. The signs of the component images will be reversed 
accordingly. The image maps and time courses of components have arbitrary units 
after the <a href="icatb_back_reconstruct.htm">back reconstruction</a> 
step and is the motivation for adding scaling step in GIFT. Presently there are 
two scaling steps like Percent Signal Change and Z-scores. </p>

<p> <b> Percent signal change </b>: The procedure for scaling individual subject 
component images and time courses is given below: </p>

<ul>
<li> Top five voxel values are determined from the component image. 
Corresponding time courses are obtained from the original subject's functional 
data using the voxel indices. </li>

<li>This data is scaled to a factor 100 times the ratio of the original time 
course with the mean of that time course for that voxel.</li>

<li> For each voxel (from the top five voxels of the component), component time 
course is fit to the scaled original time course using Multiple Regression. A 
weighted mean is taken using the weights of the top five voxels and voxel values 
for that component. </li>

<li> The component time course is scaled by multiplying component time course 
with the weighted mean.</li>

<li> The component images are scaled by a factor using the absolute value of the 
ratio of the weighted mean with the maximum voxel value. </li>

</ul>

<p class="style1"> Figure shows how calibration is done for a particular 
component: </p>

<p class="style1"> <img border="0" src="images/icatb_percent_signal_change.jpg"></p>

<p> <b> Z-Scores </b>: The component images and time courses are scaled to 
Z-scores. For each component, the time courses and spatial maps are divided with 
the their standard deviation. Figure below shows conversion to Z-scores:</p>

<p class="style1"> <img border="0" src="images/icatb_z_scores.jpg"></p>

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